Embedding Meta Information into Visualizations.

08:00 EDT 14th May 2019 | BioPortfolio

Summary of "Embedding Meta Information into Visualizations."

In this work, we study how to co-locate meta information with visualizations by directly embedding information into visualizations. This allows for visualizations to carry provenance and authorship information themselves for reproducibility. We call these self-describing visualizations -- reproducible, authenticatable, and documentable. Self-describing visualizations can be used to extend existing visualization provenance systems. Herein, we start with a survey of existing digital image watermarking literature. %methods to embed the meta information within visualizations. We search for and classify watermarking algorithms that can support scientific visualizations. Using our payload-resilience testing framework, we evaluate and recommend algorithms supporting various use cases in the payload-resiliency space, and present guidelines for optimizing visualizations to improve payload capacities and embedding robustness. We demonstrate the efficacy of self-describing visualizations with two sample application implementations: (1) adding an embedding filter as a part the standard rendering pipeline, (2) creating a web reader to automatically and reliably extract provenance information from scientific publications for review and dissemination.


Journal Details

This article was published in the following journal.

Name: IEEE transactions on visualization and computer graphics
ISSN: 1941-0506


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